Microbial function feature intelligent matching method and system

Abstract

This invention relates to the field of intelligent microbial matching technology, and more particularly to an intelligent matching method and system for microbial functional characteristics. The method includes the following steps: performing structural description feature analysis on basic data of microbial samples to generate structural description feature data of microbial functional units; analyzing microbial functional field data based on the structural description feature data of microbial functional units; performing microbial functional field response feature analysis on the microbial functional field data to generate microbial functional field response feature data; performing microbial functional realizable domain analysis based on the microbial functional field response feature data to generate microbial functional realizable domain data; and performing microbial functional feature matching optimization combination analysis on microbial functional requirement data and microbial functional realizable domain data to generate microbial functional feature matching optimization combination data. This invention achieves efficient and intelligent microbial functional feature matching.

Description

Technical Field

[0001] This invention relates to the field of intelligent matching technology for microorganisms, and in particular to an intelligent matching method and system for functional characteristics of microorganisms. Background Technology

[0002] Microorganisms, as the most widely distributed and functionally diverse group of organisms on Earth, play an irreplaceable role in various fields such as industrial fermentation, pharmaceutical research and development, environmental purification, and agricultural production through their metabolic and physiological functions. With the rapid development of technologies such as microbiome, genomics, and proteomics, there is a need for the rapid and accurate screening of microorganisms from massive microbial samples to identify those that meet specific functional requirements, achieving an efficient match between microbial functional characteristics and target needs. However, existing methods for matching microbial functions are insufficient to handle the screening demands of massive microbial samples. The limitations of single features lead to insufficient reliability of matching results. The lack of systematic analysis of the structural characteristics, action field properties, and response patterns of microbial functional units results in blind spots in the functional matching process. Furthermore, the lack of logical constraint analysis and optimized combination design for functional requirements makes it difficult to achieve synergistic matching of multi-dimensional functional characteristics, resulting in screened microorganisms failing to achieve the expected effects in industrial production. Summary of the Invention

[0003] Based on this, the present invention provides a method and system for intelligent matching of microbial functional characteristics to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for intelligent matching of microbial functional characteristics includes the following steps: Step S1: Obtain the target microbial sample and collect the basic data of the target microbial sample and the microbial functional requirement data; perform structural description feature analysis of the microbial functional units on the basic data of the microbial sample to generate structural description feature data of the microbial functional units; Step S2: Perform microbial functional field analysis based on the structural description feature data of microbial functional units to generate microbial functional field data; Step S3: Perform microbial functional field response characteristic analysis on the microbial functional field data to generate microbial functional field response characteristic data; Step S4: Perform microbial functional realization domain analysis based on microbial functional action field response characteristic data to generate microbial functional realization domain data; Step S5: Perform optimization combination analysis on the microbial functional requirement data and the microbial functional realization domain data to generate microbial functional feature matching optimization combination data.

[0005] Furthermore, step S1 includes the following steps: Step S11: Obtain the target microbial sample and collect the basic microbial sample data and microbial functional requirement data of the target microbial sample; Step S12: Perform multimodal microbial feature analysis based on the basic data of microbial samples to generate multimodal microbial feature data; Step S13: Perform microbial functional description analysis based on microbial multimodal characteristic data to generate microbial functional description data; Step S14: Perform functional description behavior parsing on the microbial functional description data to generate microbial functional description behavior data, and use the microbial functional description behavior data to establish the minimum functional unit of microbial functional description in the microbial functional description data to generate microbial functional unit description data. Step S15: Perform structural description feature analysis of microbial functional units based on the microbial functional unit description data to generate microbial functional unit structural description feature data.

[0006] Furthermore, the microbial multimodal characteristic data in step S12 includes microbial genome sequence characteristic data, microbial protein expression characteristic data, and microbial metabolite characteristic data.

[0007] Furthermore, step S2 includes the following steps: Step S21: Perform spatial coordinate mapping processing on the functional unit structure description feature data of microbial functional units to generate spatial mapping data of functional unit structure description; Step S22: Perform multi-source relationship analysis on the functional unit nodes based on the functional unit structure description spatial mapping data to generate multi-source relationship data of functional unit nodes; Step S23: Perform multi-source relation weighting processing on the multi-source relation data of functional unit nodes to generate multi-source relation weighted data of functional unit nodes; Step S24: Perform spatial neighborhood influence intensity analysis on the functional unit relationship based on the multi-source relationship weighted data of functional unit nodes, and generate spatial influence intensity data of functional unit action; Step S25: Simulate the diffusion distribution of the influence of functional units based on the spatial influence intensity data of functional units, and generate diffusion distribution data of the influence of functional units. Step S26: Analyze the microbial functional field by analyzing the diffusion distribution data of the influence of functional units, and generate microbial functional field data.

[0008] Furthermore, the multi-source relationship of functional unit nodes in step S22 includes functional unit node collaborative relationship data, functional unit node complementary relationship data, and functional unit node conflict relationship data.

[0009] Furthermore, step S3 includes the following steps: Step S31: Design microbial functional perturbation parameters using microbial functional unit structure description feature data and microbial functional field data; Step S32: Analyze the perturbation state of functional units in the microbial functional field based on the microbial functional perturbation parameters, and generate functional unit perturbation state data of the field. Step S33: Analyze the propagation impact of the disturbance state of the functional unit in the action field based on the disturbance state data of the functional unit in the action field, and generate disturbance propagation impact data of the functional unit in the action field. Step S34: Based on the disturbance propagation impact data of the functional unit of the action field, perform disturbance-specific propagation trajectory characteristic analysis of the functional unit of the action field to generate disturbance action field functional unit propagation trajectory characteristic data; Step S35: Analyze the response characteristics of the microbial functional field by using the propagation trajectory characteristic data of the perturbation field functional unit, and generate microbial functional field response characteristic data.

[0010] Furthermore, step S4 includes the following steps: Step S41: Analyze the evolution response characteristics of functional units based on the microbial functional field response characteristic data, and generate functional unit functional field evolution response characteristic data; Step S42: Extract stable evolution response features of functional units from the field evolution response feature data of functional units to generate stable evolution response feature data of functional units; Step S43: Perform extended feature analysis of the stable response of the functional field based on the stable evolution response feature data of the functional unit, and generate extended feature data of the stable response of the functional field; Step S44: Perform functional domain boundary feature analysis on microbial functional units based on the extended feature data of stable response of functional field, and generate microbial functional domain boundary feature data; Step S45: Perform microbial functional domain realization analysis based on microbial functional domain boundary feature data to generate microbial functional domain realization data.

[0011] Furthermore, step S43 includes the following steps: Step S431: Perform stable cluster analysis of microbial functional units based on the stable evolution response characteristic data of functional units to generate stable cluster data of microbial functional units; Step S432: Based on the stable cluster data of microbial functional units, the abnormal response functional units of the microbial functional field response characteristic data are screened out to generate stable response characteristic data of the functional field. Step S433: Perform cluster-related functional feature extension analysis on the stable cluster data of microbial functional units and multi-source relationship data of functional unit nodes to generate extended feature data of stable response of functional field.

[0012] Furthermore, step S5 includes the following steps: Step S51: Perform logical constraint analysis on the target functional requirements based on the microbial functional requirement data to generate logical constraint data for the target functional requirements; Step S52: Map the target functional requirement logical constraint data to the microbial functional realizable domain data, perform optimization combination analysis of microbial functional feature matching, and generate microbial functional feature matching optimization combination data.

[0013] This specification provides a microbial functional characteristic intelligent matching system for executing the microbial functional characteristic intelligent matching method as described above. The microbial functional characteristic intelligent matching system includes: The microbial functional unit description and analysis module is used to acquire target microbial samples and collect basic microbial sample data and microbial functional requirement data; it performs structural description feature analysis on the basic microbial sample data to generate microbial functional unit structural description feature data. The microbial functional field analysis module is used to perform microbial functional field analysis and processing based on the structural description feature data of microbial functional units, and generate microbial functional field data. The action field response feature analysis module is used to perform microbial functional action field response feature analysis on microbial functional action field data and generate microbial functional action field response feature data. The Functional Realization Domain Analysis module is used to perform functional realization domain analysis of microorganisms based on microbial functional action field response characteristic data, and generate microbial functional realization domain data; The microbial functional feature matching module is used to perform optimal combination analysis of microbial functional feature matching on microbial functional requirement data and microbial functional realization domain data, and generate microbial functional feature matching optimal combination data.

[0014] The beneficial effects of this application are as follows: By systematically collecting basic data and functional requirement data of target microbial samples, this invention achieves targeted matching of microbial functions, laying a precise data foundation for subsequent functional feature analysis; simultaneously, by obtaining comprehensive feature data such as microbial genome sequences, protein expression, and metabolites through multimodal feature analysis, it overcomes the limitations of single feature analysis and ensures a comprehensive capture of microbial functional characteristics; furthermore, through functional description behavior analysis and the establishment of minimum functional units, the complex microbial functions are decomposed into analyzable and describable basic units, completing the structural description feature analysis of functional units, which can accurately reflect the core structural characteristics of microbial functional units, laying the foundation for defining the achievable range of microbial functions and improving matching accuracy. Based on the structural descriptive data of microbial functional units, this study transforms the structural features of functional units into quantifiable and analyzable spatial data through spatial coordinate mapping, providing intuitive spatial dimensional support for the analysis of functional unit relationships. Multi-source relationship analysis captures various relationships among functional unit nodes, such as synergy, complementarity, and conflict, and weighting emphasizes the impact of key relationships, avoiding the one-sidedness of single-relationship analysis. Furthermore, through spatial neighborhood influence intensity analysis and diffusion distribution simulation, the study systematically analyzes the spatial influence patterns of functional unit relationships, generating microbial functional action field data. This clearly presents the scope, intensity, and mutual influence relationships of microbial functional units, improving the systematic analysis framework of microbial functional characteristics and providing core data support for subsequent functional action field response characteristic analysis and functional realizable domain definition. Based on both microbial functional unit structural description data and microbial functional field data, microbial functional perturbation parameters are designed to ensure the relevance and rationality of perturbation analysis and avoid analytical bias caused by blind perturbation. By analyzing the functional unit perturbation state, perturbation propagation impact, and perturbation propagation trajectory characteristics of the functional field, the system captures the response patterns and specific characteristics of the microbial functional field under perturbation conditions. The generated microbial functional field response characteristic data can accurately reflect the dynamic response characteristics of the microbial functional field, making up for the deficiency of not being able to systematically analyze the response patterns of the microbial functional field, further improving the systematic analysis system of microbial functional characteristics, and laying a dynamic data foundation for improving the accuracy of subsequent functional matching.Using microbial functional field response characteristic data as the core input, this study accurately screens out the core features with stability in microbial functional units through functional unit field evolution response characteristic analysis and stable evolution response feature extraction, eliminating the interference of unstable factors on functional analysis. Furthermore, through stable cluster analysis of microbial functional units, screening out abnormal response functional units, and expanding cluster-related functional features, the study further optimizes the stable response characteristic data of functional fields, ensuring the accuracy and comprehensiveness of functional feature analysis. Finally, through functional domain boundary feature analysis and functionally realizable domain analysis, the study clearly defines the realization boundaries and achievable range of microbial functions, effectively solving the problem of inaccurately defining the achievable range of microbial functions and leading to blind functional matching. Logical constraint analysis of target functional requirements is performed on microbial functional requirement data to clarify the core indicators, constraints, and priorities of the target functional requirements, avoiding problems such as ambiguity and mismatch during functional matching and ensuring the targeting of the matching analysis. Then, the logical constraint data of the target functional requirements is accurately mapped to the functionally feasible domain data of microorganisms, and an optimized combination analysis of functional feature matching is performed. This can screen out the most suitable combination of functional features from the functionally feasible range of microorganisms, solving the defects of lack of optimized combination design and inability to meet complex functional requirements. It achieves accurate and efficient matching between target functional requirements and microbial functional features, ensuring the reliability and relevance of the matching results, and meeting the refined and personalized needs of microbial functions in practical applications. This improves the practicality of microbial functional matching and provides key technical support for the efficient utilization of microbial resources.

[0015] Therefore, the intelligent matching method for microbial functional characteristics of this invention addresses the screening needs of massive microbial samples by automatically collecting basic data of microbial samples and intelligently analyzing microbial functional characteristics, ensuring matching accuracy. It comprehensively captures the functional characteristics of microorganisms, overcoming the limitation that single-feature comparison cannot fully reflect microbial functions, making the matching results more reliable and comprehensive. By analyzing the structural description features, functional field characteristics, and response laws of microbial functional units, it accurately generates realizable domain data of microbial functions, clarifies the realization boundaries of microbial functions, and achieves a precise match between target functional requirements and microbial functions. Furthermore, through logical constraint analysis and optimized combination design of functional requirements, it achieves synergistic matching of multi-dimensional functional characteristics, ensuring that the screened microorganisms achieve the expected results in industrial production. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the steps of the intelligent matching method for microbial functional characteristics according to the present invention; Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S4. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0018] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. Functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods. The term "and / or" as used herein includes any and all combinations of one or more of the associated items listed.

[0019] To achieve the above objectives, please refer to Figures 1 to 2 This invention provides a method and system for intelligent matching of microbial functional characteristics. In the embodiments of this invention, please refer to... Figure 1 The diagram shown is a flowchart illustrating the steps of an intelligent matching method for microbial functional characteristics according to the present invention. The intelligent matching method for microbial functional characteristics includes the following steps: Step S1: Obtain the target microbial sample and collect the basic data of the target microbial sample and the microbial functional requirement data; perform structural description feature analysis of the microbial functional units on the basic data of the microbial sample to generate structural description feature data of the microbial functional units; In this embodiment of the invention, the focus is on the needs of microbial screening and matching and efficient molecular docking in the biomedical field. For example, actinomycetes that can be used for antibiotic synthesis are selected as target microbial samples. Aseptic culture is used to obtain bacterial suspensions and cell samples with sufficient purity and stable activity. When collecting basic data of microbial samples, the complete genome of actinomycetes is obtained through genome extraction, functional proteins secreted by actinomycetes and related to antibiotic synthesis are obtained through protein extraction, and various metabolites in the antibiotic synthesis process are obtained through metabolite separation. At the same time, the functional requirements of microorganisms are defined as screening actinomycetes that can efficiently synthesize target antibiotics and achieve efficient docking of key proteins with antibiotic precursor molecules. The core requirements for target antibiotic synthesis and the stability requirements of molecular docking are also defined. We conduct structural descriptive feature analysis of microbial functional units based on the basic data of collected microbial samples. First, we deconstruct the complete functions related to antibiotic synthesis and molecular docking in microorganisms into multiple independent minimum functional units. Each functional unit corresponds to a core link in antibiotic synthesis and molecular docking. Then, we analyze the structural features of each minimum functional unit one by one to clarify the structural composition, core structural sites, and structural relationships of each functional unit. We integrate the structural descriptive information of all functional units to generate structural descriptive feature data of microbial functional units. This provides an accurate structural basis for subsequent analysis of microbial functional fields and supports intelligent matching of microbial functional features.

[0020] Step S2: Perform microbial functional field analysis based on the structural description feature data of microbial functional units to generate microbial functional field data; In this embodiment of the invention, based on the structural description feature data of microbial functional units, microbial functional action field analysis is carried out. The structural features of functional units are transformed into spatially analyzable correlation data, thereby constructing a complete microbial functional action field. First, the structural description feature data of microbial functional units is processed by spatial coordinate mapping, transforming the structural features of each smallest functional unit into specific coordinates and spatial ranges in three-dimensional space. This clarifies the positional distribution and relative distance of each functional unit in three-dimensional space, forming spatial mapping data of functional unit structural description. Based on this spatial mapping data, the multi-source relationships between functional unit nodes are analyzed, clarifying the synergistic, complementary, and conflicting relationships between functional units, defining the action mechanisms and results of various relationships, and generating multi-source relationship data of functional unit nodes. The multi-source relationship data is then weighted. Based on the degree of influence of each relationship on microbial antibiotic synthesis and molecular docking function, the weights of each relationship are determined, highlighting the influence of core relationships, generating weighted multi-source relationship data of functional unit nodes. Based on the weighted data, the spatial neighborhood influence intensity of functional unit relationships is analyzed, clarifying the influence intensity and range of each functional unit on other functional units within its spatial neighborhood, generating spatial influence intensity data of functional unit effects. The diffusion and distribution process of the effects of functional units is simulated to clarify the diffusion patterns, diffusion ranges, and overlapping areas of the effects of different functional units, generating diffusion and distribution data of the effects of functional units. All relevant data are integrated to construct a microbial functional action field model, clarifying the spatial range, core action area, and functional positioning of each area, generating microbial functional action field data, and fully presenting the spatial action patterns of microbial function, laying the foundation for subsequent response characteristic analysis.

[0021] Step S3: Perform microbial functional field response characteristic analysis on the microbial functional field data to generate microbial functional field response characteristic data; In this embodiment of the invention, microbial functional action field data is used as the core, combined with structural description data of microbial functional units, to conduct response characteristic analysis of the microbial functional action field. By designing targeted perturbations, the dynamic response law of the functional action field is analyzed, and the response characteristics of microbial function are explored. First, microbial functional perturbation parameters are designed, which specifically cover each smallest functional unit and the core region of the microbial functional action field, focusing on functions related to antibiotic synthesis and molecular docking. The perturbation target, perturbation range, and perturbation mode are clearly defined to ensure that the perturbation can accurately trigger the response changes of microbial function. Based on the designed perturbation parameters, the functional unit perturbation state analysis of the microbial functional action field data is performed. Various perturbation parameters are applied to the corresponding functional units and action field regions one by one, and the state changes of each functional unit, the intensity changes of the action field region, and the changes of molecular docking-related states under different perturbation conditions are recorded to generate functional unit perturbation state data of the action field. Based on disturbance state data, this study analyzes the propagation process and impact range of disturbance states between functional units and across different regions of the action field. It clarifies the propagation path, rate, and impact patterns of disturbances, elucidates the mechanism by which disturbances propagate from one functional unit to others, and generates data on the impact of disturbance propagation on functional units within the action field. For each type of disturbance, the study analyzes its specific propagation trajectory characteristics, clarifies the differences in propagation trajectories, attenuation patterns of propagation intensity, and propagation range for different disturbance types, and generates characteristic data on the propagation trajectory of functional units within the disturbance action field. Based on this propagation trajectory characteristic data, the study integrates the response patterns, response delays, and intensity change correlations of various disturbances within the functional action field. It clarifies the response characteristics of the microbial functional action field to different disturbances, elucidates the correlation mechanism between response changes and disturbance parameters, and generates characteristic data on the response of the microbial functional action field. This provides a precise dynamic response basis for subsequent analysis of the realizable domain of microbial functions.

[0022] Step S4: Perform microbial functional realization domain analysis based on microbial functional action field response characteristic data to generate microbial functional realization domain data; In this embodiment of the invention, microbial functional field response characteristic data is used as the core basis to conduct microbial functional realizable domain analysis, mine the stable evolution characteristics of functional units, define functional domain boundaries, and thus clarify the realizable range of microbial functions, supporting subsequent intelligent matching of functional features. First, based on the microbial functional field response characteristic data, the evolutionary response characteristics of each functional unit are analyzed, tracking the response changes of each functional unit within a set evolutionary period. The evolutionary laws, evolutionary stages, and response characteristics of different evolutionary stages of each functional unit are clarified, and the dynamic relationship between functional units and the field of action during evolution is defined, generating functional unit field of action evolution response characteristic data. Then, stable evolutionary response characteristics of functional units are extracted from the evolutionary response characteristic data. The stages in which each functional unit is in a stable state during evolution and their corresponding stable response characteristics are selected, unstable response data during evolution are eliminated, and the core characteristics and characteristic range of stable responses of each functional unit are clarified, generating functional unit stable evolutionary response characteristic data. Based on stable evolutionary response characteristic data and combined with multi-source relationship data of functional unit nodes, an extended characteristic analysis of stable response in functional action fields is conducted. First, stable cluster analysis is performed on functional units, grouping those with closely related functions and similar stable characteristics into one category, generating stable cluster data for microbial functional units. Then, based on the stable cluster data, anomalous response functional unit data in the action field are filtered out, retaining stable response data. Further, the linkage relationships within stable clusters are explored, expanding the coverage of stable response characteristics and generating extended characteristic data of stable response in functional action fields. Based on the extended stable response characteristic data, the functional domain boundary characteristics of microbial functional units are analyzed, clarifying the spatial, characteristic, and functional boundaries of each functional unit and stable cluster, defining the functional range and action boundaries of each functional unit, and generating microbial functional domain boundary characteristic data. Based on the functional domain boundary characteristic data and combined with the core directions of microbial functional requirements, the achievable functional range of each functional unit is clarified. The achievable functions of all functional units are integrated, defining the spatial range, core functions, and functional realization conditions of the microbial functional achievable domains, generating microbial functional achievable domain data. This clearly presents the achievable functional range of microorganisms in antibiotic synthesis and efficient molecular docking, providing a basis for subsequent functional feature matching and optimization.

[0023] Step S5: Perform optimization combination analysis on the microbial functional requirement data and the microbial functional realization domain data to generate microbial functional feature matching optimization combination data.

[0024] In this embodiment of the invention, the core objective of intelligent matching of microbial functional characteristics is focused. Combining microbial functional requirement data and microbial functional realizable domain data, an optimized combination analysis of microbial functional characteristic matching is conducted to achieve precise matching between the functional characteristics of actinomycetes in the biopharmaceutical field and the needs of antibiotic synthesis and efficient molecular docking. First, based on the microbial functional requirement data, a logical constraint analysis of the target functional requirements is conducted. The core indicators of the target functional requirements are decomposed, the constraints of each core indicator and the logical relationships between indicators are clarified, and the synergistic constraint relationships between indicators are defined, forming a complete logical constraint chain. The core constraint standards for functional characteristic matching are defined to ensure that the constraint analysis aligns with the actual needs of antibiotic synthesis and efficient molecular docking, generating logical constraint data for the target functional requirements. The logical constraint data of the target functional requirements is then accurately mapped to the microbial functional realizable domain data. An optimized combination analysis of microbial functional characteristic matching is then conducted. Based on the logical constraint standards, the optimal feature parameters of each functional unit are mined from the functional realizable domain and integrated to form the functional feature combination that best matches the target functional requirements. This study analyzes the compatibility between the stable response characteristics of each functional unit and the target functional requirements. Combining the multi-source relationships between functional unit nodes and the linkage patterns of stable clusters, it selects the feature parameters in each functional unit that best satisfy logical constraints and are most conducive to achieving efficient antibiotic synthesis and molecular docking. The optimal feature parameters of each functional unit are integrated to form the optimal combination of microbial functional features. The functional performance, molecular docking stability, and antibiotic synthesis efficiency corresponding to this combination are clarified, ensuring that the combination fully meets the logical constraints of the target functional requirements. By integrating all relevant information, matching processes, and matching criteria for the optimal functional feature combination, optimized combination data of microbial functional features is generated. This enables precise and intelligent matching of microbial functional features with the needs of antibiotic synthesis and efficient molecular docking in the biomedical field, providing reliable technical support for medical microbial screening and practical applications.

[0025] Furthermore, step S1 includes the following steps: Step S11: Obtain the target microbial sample and collect the basic microbial sample data and microbial functional requirement data of the target microbial sample; In this embodiment of the invention, actinomycetes suitable for antibiotic synthesis were selected as target microbial samples. Single colonies were picked from actinomycete slant culture medium using a sterile inoculation loop and inoculated into LB liquid medium. The culture was then shaken at 37°C and 180 r / min for 24 h to obtain bacterial culture samples. When collecting basic data of microbial samples, genomic material of actinomycetes was extracted using a genomic extraction kit, functional proteins secreted by actinomycetes were extracted using a protein extraction kit, and antibiotic precursors produced by actinomycete metabolism were separated using high performance liquid chromatography. At the same time, the functional requirements of microorganisms were defined as screening actinomycetes that can efficiently synthesize the target antibiotic (penicillin) and achieve efficient molecular docking. Specifically, the antibiotic synthesis yield was not less than 150 mg / L, the molecular docking binding energy was not higher than -8.0 kcal / mol, and the fermentation cycle was not more than 48 hours. The collected genomic material, functional protein, antibiotic precursor, and functional requirement parameters were uniformly organized to form complete basic data of microbial samples and functional requirement data of microorganisms.

[0026] Step S12: Perform multimodal microbial feature analysis based on the basic data of microbial samples to generate multimodal microbial feature data; In this embodiment of the invention, multimodal microbial characteristic analysis was performed based on basic microbial sample data. Microbial genome sequence characteristic data were obtained using Sanger sequencing. Sequencing primers were selected from the V1-V2 region of the actinomycete 16S rRNA gene. Amplification conditions were set as follows: pre-denaturation at 95°C for 5 min, denaturation at 95°C for 30 s, annealing at 58°C for 30 s, extension at 72°C for 45 s, repeated 35 times, followed by a final extension at 72°C for 10 min. The complete actinomycete genome sequence was obtained through sequencing, and characteristic parameters such as gene sequences related to penicillin synthesis, GC content, coding region length, and number of intergenic regions were extracted. Microbial protein expression characteristic data were obtained using two-dimensional gel electrophoresis. The electrophoresis voltage was set to 80V for the stacking gel and 120V for the separating gel, with electrophoresis times of 30 min for the stacking gel and 90 min for the separating gel. We obtained characteristic parameters such as molecular weight, isoelectric point, and expression level of functional proteins related to penicillin synthesis, focusing on capturing the expression characteristics of key enzymes in penicillin synthesis (penicillin synthase). Microbial metabolite characteristic data were determined by high-performance liquid chromatography (HPLC) using a C18 column, a methanol-water (55:45 v / v) mobile phase, a flow rate of 1 mL / min, and a detection wavelength of 270 nm. We obtained characteristic parameters such as retention time, peak area, peak height, and concentration of penicillin precursor (6-aminopenicillanic acid). We integrated these three types of characteristic parameters to form multimodal microbial characteristic data, providing a foundation for subsequent molecular docking-related functional analysis.

[0027] Step S13: Perform microbial functional description analysis based on microbial multimodal characteristic data to generate microbial functional description data; In this embodiment of the invention, microbial functional description analysis is performed based on microbial multimodal characteristic data. Combining the coding region sequences in the microbial genome sequence characteristics, functional genes related to penicillin synthesis in actinomycetes (including penicillin synthase genes and cyclase genes) are identified, clarifying the metabolic pathways corresponding to each functional gene and defining the relationship between gene expression and penicillin synthesis. Combining the expression levels of functional proteins in the microbial protein expression characteristics, key proteins involved in penicillin synthesis (penicillin synthase and cyclase) are identified, clarifying the action sites and mechanisms of action of these key proteins. The binding sites between these key proteins and penicillin precursors are analyzed, providing a basis for efficient molecular docking. Combining the types and concentrations of antibiotic precursors in the microbial metabolite characteristics, intermediate products, precursors, and final products in the penicillin synthesis process are determined, clarifying the material transformation rules of the metabolic process and mastering the key links in the transformation of penicillin precursors into final products. By integrating functional genes, key proteins, metabolites, related action rules, and molecular binding site information, complete microbial functional description data is formed, clearly presenting the functional performance of actinomycetes in penicillin synthesis and molecular docking.

[0028] Step S14: Perform functional description behavior parsing on the microbial functional description data to generate microbial functional description behavior data, and use the microbial functional description behavior data to establish the minimum functional unit of microbial functional description in the microbial functional description data to generate microbial functional unit description data. In this embodiment of the invention, microbial functional description data is used as the core to conduct functional description behavior analysis and establish minimum functional units. For example, focusing on penicillin synthesis and efficient molecular docking, complex microbial functions are decomposed into minimum functional units that can be accurately analyzed and studied independently, providing support for subsequent functional unit structure analysis and functional matching. First, the microbial functional description data undergoes functional description behavior analysis, comprehensively decomposing all behaviors related to penicillin synthesis and molecular docking involved in the microbial functional description data. Specifically, this includes the transcriptional behavior of functional genes related to penicillin synthesis, the synthesis and action of key proteins, the generation and transformation of penicillin precursor substances, and the binding behavior of key proteins and precursor substances, among other core behaviors. Each type of behavior is analyzed in detail to clarify the triggering conditions of each behavior, i.e., under what circumstances the behavior will be initiated; to clarify the execution process of each behavior, i.e., the specific steps, participating substances, and mechanisms of action; and to clarify the results of each behavior, i.e., the specific impact of the behavior on penicillin synthesis and molecular docking. The focus is on analyzing the binding mechanism of key proteins and precursor substances, clarifying the molecular action principle in the binding process, and generating comprehensive and detailed microbial functional description behavior data. Based on the microbial functional description data, the minimum functional units for microbial functional description were established. The core logic is to decompose the complex overall function of penicillin synthesis and efficient molecular docking into three interconnected and cooperating minimum functional units. Each minimum functional unit corresponds to a core step in penicillin synthesis and molecular docking, ensuring that each unit has a single and clear function, facilitating subsequent structural analysis and functional matching. The three minimum functional units are: functional gene transcription unit, key protein action unit, and precursor material conversion and molecular docking unit. The functional gene transcription unit corresponds to specific coding regions related to penicillin synthesis in the genome sequence, mainly responsible for the transcription of functional genes related to penicillin synthesis, providing gene templates for the synthesis of key proteins. The key protein action unit corresponds to key enzymes in penicillin synthesis with specific molecular weights and isoelectric points, mainly responsible for catalyzing the conversion of penicillin precursor materials and participating in the molecular docking process. The precursor material conversion and molecular docking unit corresponds to the conversion process of penicillin precursor materials and the binding process of key proteins with precursor materials, mainly responsible for realizing the conversion of precursor materials into penicillin end products, while ensuring the stability of efficient molecular docking. For each minimum functional unit, the corresponding characteristic parameters and behavioral boundaries are clearly defined, and the functional scope and action boundary of each unit are defined to avoid functional overlap or functional loss. Then, the relevant information, characteristic parameters and behavioral boundaries of the three minimum functional units are integrated to generate complete microbial functional unit description data, which provides accurate and reliable support for subsequent functional unit structure description feature analysis and molecular docking-related functional matching.

[0029] Step S15: Perform structural description feature analysis of microbial functional units based on the microbial functional unit description data to generate microbial functional unit structural description feature data.

[0030] In this embodiment of the invention, based on the description data of microbial functional units, structural descriptive feature analysis of microbial functional units is carried out. Structural descriptive feature analysis is performed on each smallest functional unit individually to ensure the relevance and detail of the analysis. For functional gene transcription units, the core structural features of their coding regions are analyzed, including key structural parameters such as the base sequence, gene length, and promoter position. The specific base sequence in the base sequence is set as AGCTGACC, which is the core coding sequence of the penicillin synthase gene and directly determines the amino acid sequence and functional activity of penicillin synthase. The gene length is set to 1300 bp to clarify the specific length range of the coding region. The promoter is set to be located 120 bp upstream of the coding region to clarify its specific position. As a regulatory site for gene transcription, the promoter directly affects gene transcription efficiency, and thus penicillin synthesis efficiency. For the key protein's functional unit, we focused on analyzing its core structural features, including key parameters such as amino acid sequence, spatial conformation, and the location of the active site. The amino acid sequence was defined as Met-Gly-Thr-Phe, the core amino acid sequence of penicillin synthase, which determines the key protein's spatial conformation and catalytic activity. The spatial conformation was defined as a mixed structure of α-helix and β-sheet, which ensures the key protein's structural stability and catalytic activity. The active site was located at positions 25-45 of the amino acid sequence; this active site is the molecular docking binding site with penicillin precursors, directly determining the stability and efficiency of molecular docking, and is the core structural basis for efficient molecular docking. This study focuses on the precursor material transformation and molecular docking unit, analyzing the core structural features of the penicillin precursor (6-aminopenicillanic acid), including key parameters such as material structure, functional group type, and molecular docking site. The core material structure of the precursor is defined as a β-lactam ring structure, which is the core structure of the penicillin molecule and directly determines its antibacterial activity. The functional groups are defined as amino and carboxyl groups, which are important sites for the binding of the precursor to key proteins and affect the stability of molecular docking. The molecular docking site is defined as the nitrogen atom on the β-lactam ring, clarifying the specific site for molecular docking and providing a precise structural reference for subsequent analysis and verification of efficient molecular docking. Finally, all structural feature parameters of the three smallest functional units are comprehensively integrated, clarifying the relationships between the structural features of each functional unit, defining the correlation between structural features and functional performance and molecular docking, generating complete and accurate structural description data of microbial functional units, clearly defining key molecular docking sites and structural parameters, and providing a solid structural foundation for subsequent analytical steps.

[0031] Furthermore, the microbial multimodal characteristic data in step S12 includes microbial genome sequence characteristic data, microbial protein expression characteristic data, and microbial metabolite characteristic data.

[0032] Furthermore, step S2 includes the following steps: Step S21: Perform spatial coordinate mapping processing on the functional unit structure description feature data of microbial functional units to generate spatial mapping data of functional unit structure description; In this embodiment of the invention, the structural descriptive data of microbial functional units are processed by spatial coordinate mapping of functional unit structures. Focusing on the three smallest functional units related to penicillin synthesis (functional gene transcription unit, key protein action unit, and precursor material conversion and molecular docking unit), a three-dimensional spatial coordinate modeling method is used to convert the structural feature parameters of each functional unit into three-dimensional spatial coordinate parameters. The spatial coordinate system is set as a Cartesian coordinate system, with the active center of the key protein action unit as the origin (0,0,0). The promoter position of the functional gene transcription unit is mapped to coordinates (-50,20,10), and the core sequence of the coding region is mapped to coordinates ranging from (-45,20,10) to (1255,20,10). The active center of the key protein action unit is mapped to coordinates (0,0,0), and the α-helix structure is mapped to coordinates ranging from (-20,-15,-10) to (30,15,10). The mapping coordinates of the β-lactam ring core structure of the mass transfer and molecular docking unit are (30, 40, 20), and the mapping coordinates of the molecular docking site (nitrogen atom on the β-lactam ring) are (32, 42, 21). The specific location, spatial range and relative distance of each functional unit structure in three-dimensional space are clearly defined. All spatial coordinate parameters are integrated to generate spatial mapping data describing the functional unit structure, realizing the spatial visualization of the functional unit structure, and laying a spatial foundation for subsequent analysis of functional unit node relationships and molecular docking-related action field analysis.

[0033] Step S22: Perform multi-source relationship analysis on the functional unit nodes based on the functional unit structure description spatial mapping data to generate multi-source relationship data of functional unit nodes; In this embodiment of the invention, multi-source relationship analysis of functional unit nodes is performed based on the spatial mapping data of the functional unit structure description. Three minimum functional units are each treated as independent functional unit nodes: a functional gene transcription node, a key protein interaction node, and a precursor material transformation and molecular docking node. By analyzing the coordinate positions, structural feature associations, and functional associations of each node in three-dimensional space, three types of multi-source relationships are identified. Each of the three minimum functional units is treated as an independent functional unit node, with nodes defined as functional gene transcription nodes, key protein interaction nodes, and precursor material transformation and molecular docking nodes. Through systematic analysis of the coordinate positions, structural feature associations, and functional associations of each node in three-dimensional space, three types of multi-source relationships are precisely defined. Clear judgment criteria are established for each type of relationship in conjunction with the requirements of molecular docking and penicillin synthesis. The core of the synergistic relationship is manifested in the gene sequence transcribed from the functional gene transcription node, which directly guides the synthesis of the key enzyme required for penicillin synthesis at the key protein action node. The key enzyme at the key protein action node further catalyzes the conversion of precursor substances at the molecular docking node. The criterion for determining the synergistic relationship is that the spatial distance between the two nodes does not exceed 100 coordinate units and their functions are directly related, ensuring the effectiveness and specificity of the synergistic relationship. The complementary relationship is manifested in the positive correlation between the gene expression level of the functional gene transcription node and the protein expression level of the key protein action node, and the positive correlation between the enzyme activity of the key protein action node and the conversion efficiency of the precursor substance conversion and the molecular docking node. The two support each other. The study explores three types of relationships: 1. Mutual promotion and joint improvement of penicillin synthesis efficiency and molecular docking stability. A complementary relationship is defined as a correlation coefficient of at least 0.85, clarifying the strength requirements for complementary associations. 2. Conflict relationships are characterized by excessively high transcription efficiency at functional gene transcription nodes, leading to excessive protein synthesis at key protein interaction nodes. This excess key protein occupies molecular docking sites of precursor substances, inhibiting precursor conversion and molecular docking efficiency, thus affecting normal penicillin synthesis. A conflict relationship is defined as a decrease in molecular docking efficiency exceeding 20% ​​when functional gene transcription efficiency exceeds 1500 bp / min, clarifying the triggering conditions and impact of conflict relationships. The results, association mechanisms, and related parameters of these three types of relationships are comprehensively integrated to generate multi-source relationship data for functional unit nodes, clearly presenting the interaction patterns between each functional unit node. This provides precise relationship support for functional matching and subsequent weighting processing for efficient molecular docking.

[0034] Step S23: Perform multi-source relation weighting processing on the multi-source relation data of functional unit nodes to generate multi-source relation weighted data of functional unit nodes; In this embodiment of the invention, multi-source relationship data of functional unit nodes are processed by multi-source relationship weighting. The analytic hierarchy process (AHP) is used to determine the weights of each type of multi-source relationship. Considering the core needs of microbial screening and molecular docking in the biomedical field, the weights are determined based on the degree of influence of the relationship on penicillin synthesis efficiency and molecular docking effectiveness. Synergistic relationships directly determine the integrity of the penicillin synthesis chain and the effectiveness of molecular docking, forming the core foundation for efficient penicillin synthesis and molecular docking. They have the most significant impact on overall functional effects and are assigned a weight of 0.5. Complementary relationships, by regulating the correlation between gene expression levels, enzyme activity, and material conversion efficiency, directly affect penicillin synthesis efficiency and molecular docking stability, providing important support for improving functional effects. They are assigned a weight of 0.35. Conflicting relationships interfere with the normal progress of penicillin synthesis and molecular docking, disrupting functional balance and negatively impacting overall functional effects. Their impact is relatively low, and they are assigned a weight of 0.15. The total weight of the three types of relationships is 1, ensuring the rationality and scientific nature of the weight allocation. Subsequently, weighted calculations were performed on the specific relationships between each functional unit node. The synergistic, complementary, and conflicting relationships between functional gene transcription nodes and key protein interaction nodes, key protein interaction nodes and precursor material transformation and molecular docking nodes, and functional gene transcription nodes and precursor material transformation and molecular docking nodes were assigned values ​​of 0.5, 0.35, and 0.15 respectively, ensuring consistent weighting standards and logic. All weighting results were categorized, organized, and archived to generate multi-source relationship weighted data for functional unit nodes, clearly presenting the weight percentage of each type of relationship. This highlighted the core impact of synergistic and complementary relationships on microbial function and molecular docking, providing reliable quantitative support for the accurate calculation of subsequent spatial neighborhood influence intensity.

[0035] Step S24: Perform spatial neighborhood influence intensity analysis on the functional unit relationship based on the multi-source relationship weighted data of functional unit nodes, and generate spatial influence intensity data of functional unit action; In this embodiment of the invention, the spatial neighborhood influence intensity analysis of the functional unit relationship is performed based on the multi-source relationship weighted data of the functional unit nodes. A spatial neighborhood influence model is adopted, with each functional unit node as the center and the spatial neighborhood radius set to 150 coordinate units, to analyze the influence intensity of each node on other nodes within its spatial neighborhood. The neighborhood influence strength of functional gene transcription nodes on key protein interaction nodes is calculated as the cooperative relationship weight × the inverse of the spatial distance. With a spatial distance of 50 coordinate units, the calculated influence strength is 0.5 × (1 / 50) = 0.01. The neighborhood influence strength of key protein interaction nodes on precursor transformation and molecular docking nodes is calculated as the cooperative relationship weight × the inverse of the spatial distance. With a spatial distance of 80 coordinate units, the calculated influence strength is 0.5 × (1 / 80) = 0.00625. The neighborhood influence strength of functional gene transcription nodes on precursor transformation and molecular docking nodes is calculated as the cooperative relationship weight × the inverse of the spatial distance. With a spatial distance of 120 coordinate units, the calculated influence strength is 0.5 × (1 / 120) ≈ 0.00417. Simultaneously, by combining the weights of complementary and conflicting relationships, the influence intensity values ​​are adjusted. Complementary relationships increase the influence intensity by 20%, while conflicting relationships decrease the influence intensity by 15%. Finally, the spatial neighborhood influence intensity values ​​between each node are determined, and all values ​​are integrated to generate spatial influence intensity data of functional unit actions. This clarifies the spatial influence range and intensity of the relationships between each functional unit, providing a quantitative basis for subsequent action field analysis.

[0036] Step S25: Simulate the diffusion distribution of the influence of functional units based on the spatial influence intensity data of functional units, and generate diffusion distribution data of the influence of functional units. In this embodiment of the invention, the diffusion distribution of the influence of functional units is simulated based on the spatial influence intensity data of functional units. A Gaussian diffusion model is used, with a diffusion coefficient set to 0.05 and a diffusion time of 24 hours. Each functional unit node is used as the diffusion center to simulate the spatial diffusion process of the relationships between nodes. The influence of functional gene transcription nodes diffuses outwards from the origin (-50, 20, 10) according to a Gaussian distribution. The diffusion intensity decreases exponentially with increasing distance; the diffusion intensity at a distance of 100 coordinate units from the center is 50% of the central intensity, and at a distance of 150 coordinate units from the center, the diffusion intensity is 20% of the central intensity. The influence of key protein interaction nodes is centered at the origin (0, 0, 0), and the diffusion pattern is consistent with that of functional gene transcription nodes, with the diffusion intensity at a distance of 100 coordinate units from the center being 50% of the central intensity. The influence of precursor material transformation and molecular docking nodes is centered at coordinates (30, 40, 20), with the same diffusion pattern, and the diffusion intensity at a distance of 100 coordinate units from the center being 50% of the central intensity. During the simulation, the influence intensity distribution, diffusion range, and overlapping areas of diffusion influence of different nodes in each diffusion region were clearly defined. The influence intensity of the overlapping area is the sum of the diffusion intensity of each node. The overlapping area is set to be mainly concentrated between coordinates (0,20,15) and (30,30,20). This area is the core area of ​​penicillin synthesis and molecular docking. All diffusion distribution parameters were recorded to generate diffusion distribution data of the influence of functional unit effects, clearly presenting the spatial diffusion law of the functional unit relationship.

[0037] Step S26: Analyze the microbial functional field by analyzing the diffusion distribution data of the influence of functional units, and generate microbial functional field data.

[0038] In this embodiment of the invention, microbial functional field analysis is conducted by analyzing the diffusion distribution data influenced by functional units. The spatial coordinates of each functional unit node, multi-source relationship weighting, spatial neighborhood influence intensity, and diffusion distribution patterns are integrated to construct a microbial functional field model. This model uses three-dimensional space as its framework, defining the spatial range of the functional field as coordinates (-200, -100, -50) to (300, 200, 150), and dividing it into three core functional regions: the gene transcription region, the protein catalysis region, and the molecular docking and material transformation region. The gene transcription region, centered on functional gene transcription nodes, covers an area with a diffusion intensity no less than 30% of the central intensity, primarily realizing functional gene transcription and regulation. The protein catalysis region, centered on key protein nodes, covers an area with a diffusion intensity no less than 30% of the central intensity, primarily realizing the catalytic action of key enzymes and the initial binding of molecular docking. The molecular docking and material transformation region, centered on precursor material transformation and molecular docking nodes, covers an area with a diffusion intensity no less than 30% of the central intensity, primarily realizing precursor material transformation and efficient molecular docking between key proteins and precursor materials. The intensity, functional positioning, and interrelationships of each region are clearly defined, and the core parameters of the functional action field are determined, including the range of action field intensity, the coordinates of the core action area, and the functional correlation rules of each region. All parameters are integrated to generate microbial functional action field data, which fully presents the spatial distribution of the functional action of actinomycetes in the penicillin synthesis process and the action rules related to molecular docking, providing a precise action field basis for the response characteristic analysis in the subsequent step S3.

[0039] Furthermore, the multi-source relationship of functional unit nodes in step S22 includes functional unit node collaborative relationship data, functional unit node complementary relationship data, and functional unit node conflict relationship data.

[0040] Furthermore, step S3 includes the following steps: Step S31: Design microbial functional perturbation parameters using microbial functional unit structure description feature data and microbial functional field data; In this embodiment of the invention, based on both microbial functional unit structural description data and microbial functional field data, microbial functional perturbation parameters are designed. These perturbation parameters focus on functions related to penicillin synthesis and efficient molecular docking in actinomycetes. Combining functional unit structural characteristics and core parameters of the functional field, three types of targeted perturbation parameters are set, corresponding to the three smallest functional units and the core regions of the microbial functional field, respectively. For functional gene transcription units, transcription efficiency perturbation parameters are designed, with a perturbation range of 80%-120% of normal transcription efficiency, a perturbation step size of 10%, and the perturbation target being the promoter region of the functional gene transcription unit. For key protein action units, enzyme activity perturbation parameters are designed, with a perturbation range of 70%-130% of normal enzyme activity, a perturbation step size of 10%, and the perturbation target being the active site of the key protein (amino acid positions 25-45). For precursor material transformation and molecular docking units, molecular docking efficiency perturbation parameters are designed, with a perturbation range of 75%-125% of normal docking efficiency. The perturbation step size is 5%, and the perturbation target is the molecular docking site (nitrogen atom on the β-lactam ring) of the precursor substance (6-aminopenicillanic acid). At the same time, perturbation parameters for the functional interaction field strength are designed, with the perturbation range set at 85%-115% of the normal interaction field strength and the perturbation step size at 5%. The perturbation region covers the three core regions of the microbial functional interaction field. All perturbation parameters and corresponding perturbation targets, perturbation ranges, and perturbation step sizes are integrated to form complete microbial functional perturbation parameters. This ensures that the perturbation is specifically tailored to the core requirements of penicillin synthesis and efficient molecular docking, and provides clear parameter basis for subsequent perturbation state analysis.

[0041] Step S32: Analyze the perturbation state of functional units in the microbial functional field based on the microbial functional perturbation parameters, and generate functional unit perturbation state data of the field. In this embodiment of the invention, the functional unit perturbation state of the microbial functional action field data is analyzed based on microbial functional perturbation parameters. Various perturbation parameters are applied to the corresponding functional units and functional action field regions one by one, and the state changes of the functional units and action fields under each perturbation condition are recorded. When applying the transcription efficiency perturbation parameter, when the transcription efficiency drops to 80% of the normal level, the transcription rate of the coding region of the functional gene transcription unit decreases, the corresponding gene expression level decreases, and the intensity of the gene transcription action region in the functional action field decreases; when the transcription efficiency rises to 120% of the normal level, the transcription rate of the coding region accelerates, the gene expression level increases, and the intensity of the gene transcription action region increases. When applying the enzyme activity perturbation parameter, when the enzyme activity drops to 70% of the normal level, the catalytic ability of the key protein action unit decreases, the molecular docking binding stability decreases, and the intensity of the protein catalytic action region decreases; when the enzyme activity rises to 130% of the normal level, the catalytic ability of the key protein increases, the molecular docking binding stability improves, and the intensity of the protein catalytic action region increases. When molecular docking efficiency perturbation parameters are applied, if the docking efficiency drops to 75% of the normal level, the precursor material conversion rate decreases, and the intensity of the molecular docking and material conversion region decreases; if the docking efficiency rises to 125% of the normal level, the precursor material conversion rate accelerates, and the intensity of this region increases. When interaction field intensity perturbation parameters are applied, the intensity changes and regional range shifts of the three core interaction regions are recorded. By integrating the functional unit states, interaction field region states, molecular docking states, and related change parameters under all perturbation conditions, interaction field functional unit perturbation state data are generated, clearly presenting the functional response changes under different perturbation conditions.

[0042] Step S33: Analyze the propagation impact of the disturbance state of the functional unit in the action field based on the disturbance state data of the functional unit in the action field, and generate disturbance propagation impact data of the functional unit in the action field. In this embodiment of the invention, based on the disturbance state data of the functional units of the action field, the propagation impact analysis of the disturbance state of the functional units of the action field is carried out. A spatial propagation model is adopted, with the propagation rate set at 5 coordinate units / h and the propagation attenuation coefficient at 0.02. Taking the disturbance target point of each functional unit as the propagation starting point, the propagation process and influence range of the disturbance state between functional units and between different regions of the functional action field are analyzed. Transcription efficiency perturbations propagate from the functional gene transcription unit (coordinates -50, 20, 10) to the key protein action unit (coordinates 0, 0, 0). Upon reaching the key protein action unit, the amount of key protein synthesized changes with transcription efficiency, thus affecting molecular docking efficiency. Enzyme activity perturbations propagate from the key protein action unit to the molecular docking and material conversion region (coordinates 30, 40, 20), causing changes in precursor material conversion rate and molecular docking stability. Simultaneously, they propagate back to the functional gene transcription unit, regulating gene expression. Molecular docking efficiency perturbations propagate from the precursor material conversion and molecular docking unit to the key protein action unit, affecting the regulation of key protein activity. Simultaneously, they propagate to the gene transcription action region, providing feedback regulation of functional gene transcription efficiency. Action field intensity perturbations propagate among the three core regions, causing synergistic changes in intensity across regions. During propagation, the time, distance, and intensity of perturbation propagation, as well as the response changes of each functional unit and action field region, are recorded to clarify the path, rate, and impact patterns of perturbation propagation. All propagation-related data are integrated to generate data on the impact of perturbation propagation on functional units within the action field.

[0043] Step S34: Based on the disturbance propagation impact data of the functional unit of the action field, perform disturbance-specific propagation trajectory characteristic analysis of the functional unit of the action field to generate disturbance action field functional unit propagation trajectory characteristic data; In this embodiment of the invention, based on the data on the propagation impact of perturbations in functional units of the action field, a perturbation-specific propagation trajectory feature analysis of functional units of the action field is conducted. For the perturbation propagation process corresponding to each type of perturbation parameter, the core feature parameters of the propagation trajectory are extracted to clarify the differences in propagation trajectories for different perturbation types. The propagation trajectory of transcription efficiency perturbation is a linear propagation from functional gene transcription units to key protein action units, and then to molecular docking and material conversion regions. The trajectory equation is set as y = 0.4x - 20 (x is the abscissa, y is the ordinate), the width of the propagation trajectory is 10 coordinate units, and the perturbation intensity decays uniformly during the propagation process, with a 10% decrease in intensity every 50 coordinate units. The propagation trajectory of enzyme activity perturbation is a bidirectional radiative propagation from key protein action units to molecular docking and material conversion regions and functional gene transcription units. The trajectory equation is set as x² + y² = 1000 (x is the abscissa, y is the ordinate), with the width of the propagation trajectory being 10 coordinate units. The propagation trajectory of the perturbation with the center is y = 0.3x + 31, with a width of 8 coordinate units and a gradient decrease in propagation intensity from the center outwards. The propagation trajectory of the molecular docking efficiency perturbation is a unidirectional propagation from the precursor material conversion and molecular docking unit to the key protein action unit, with the trajectory equation set as y = 0.3x + 31 and a width of 12 coordinate units. The propagation intensity decays faster than the other two types of perturbations. The propagation trajectory of the action field intensity perturbation is a cyclical propagation between the three core regions, with a triangular trajectory whose vertices are the center coordinates of the three core regions. The propagation trajectory width is 15 coordinate units, and the propagation intensity decays uniformly. The equations, widths, decay patterns, and propagation directions of various perturbation trajectories are extracted and integrated to form characteristic data of the propagation trajectory of the perturbation action field functional unit, clarifying the specific propagation laws of different perturbations.

[0044] Step S35: Analyze the response characteristics of the microbial functional field by using the propagation trajectory characteristic data of the perturbation field functional unit, and generate microbial functional field response characteristic data.

[0045] In this embodiment of the invention, the response characteristics of the microbial functional field are analyzed by using the propagation trajectory characteristic data of the perturbation field functional unit. By combining the propagation trajectory, perturbation intensity and state changes of the corresponding functional unit and field area of ​​various perturbations, the response law of the microbial functional field is clarified. For perturbations in transcription efficiency, the response characteristics of the functional field were clarified as follows: the intensity of the gene transcription region changes synchronously with transcription efficiency, with a response delay of 2 hours. For every 10% change in transcription efficiency, the intensity of the gene transcription region changes by 8%, simultaneously driving a coordinated change in the intensity of the protein catalytic region, molecular docking, and substance transformation region. For perturbations in enzyme activity, the response characteristics of the functional field were as follows: the intensity of the protein catalytic region changes synchronously with enzyme activity, with a response delay of 1.5 hours. For every 10% change in enzyme activity, the intensity of the protein catalytic region changes by 12%, and the molecular docking binding energy changes synchronously by 0.5 kcal / mol. For perturbations in molecular docking efficiency, the response characteristics of the functional field were as follows: the intensity of the molecular docking and substance transformation regions changes synchronously with docking efficiency, with a response delay of 1 hour. For every 5% change in docking efficiency, the intensity of this region changes by 6%, and the precursor substance transformation rate changes synchronously by 8%. For perturbations in the intensity of the functional field, the response characteristics were as follows: the intensity of the three core regions changes synchronously, with no significant response delay. For every 5% change in the intensity of the functional field, the intensity of each region changes synchronously by 5%. By integrating the response patterns, response delay times, intensity change ratios, and molecular docking-related response parameters of various perturbations in the functional field, microbial functional field response characteristic data are generated. This clearly presents the response characteristics of the microbial functional field to different perturbations, providing accurate dynamic response basis for subsequent analysis of the functional realizable domain of microorganisms, intelligent matching of functional characteristics, and optimization of efficient molecular docking.

[0046] Furthermore, as an embodiment of the present invention, reference is made to... Figure 2 As shown, Figure 1 A detailed flowchart illustrating the implementation steps of step S4 is provided in this embodiment. Step S4 includes: Step S41: Analyze the evolution response characteristics of functional units based on the microbial functional field response characteristic data, and generate functional unit functional field evolution response characteristic data; In this embodiment of the invention, based on the microbial functional field response characteristic data, the evolution response characteristics of the functional unit's field are analyzed. Focusing on the three smallest functional units related to penicillin synthesis and efficient molecular docking in actinomycetes, the functional field response patterns, response delay time, and intensity change ratios corresponding to various perturbations are combined to construct a functional unit's field evolution model. The evolution analysis period is set to 48 hours, and the evolution time step is 2 hours to track the field response changes of each functional unit within the evolution period. For functional gene transcription units, the evolutionary response of their action field under transcription efficiency perturbation was analyzed. Changes in the intensity of gene transcription action regions and gene expression levels at different time points were recorded to clarify the dynamic correlation between transcription efficiency and action field intensity during evolution. It was set that the response to transcription efficiency perturbation showed a linear change in the early stage (0-12h), tended to stabilize in the middle stage (12-36h), and slightly decreased in the later stage (36-48h). For key protein action units, the evolutionary response of their action field under enzyme activity perturbation was analyzed. Changes in the intensity of protein catalytic action regions, enzyme activity, and molecular docking binding energy at different time points were recorded to clarify the dynamic correlation between enzyme activity and molecular docking stability during evolution. It was set that enzyme activity and molecular docking binding energy were positively correlated throughout the evolution process. For precursor material conversion and molecular docking units, the evolutionary response of their action field under molecular docking efficiency perturbation was analyzed. Changes in the intensity of molecular docking and material conversion regions and precursor material conversion rates at different time points were recorded to clarify the dynamic correlation between docking efficiency and material conversion rate during evolution. It was set that for every 10% increase in docking efficiency during evolution, the material conversion rate would increase by 8%. By integrating the evolutionary response data, evolutionary patterns, and dynamic correlation parameters of the three functional units, evolutionary response characteristic data of the functional unit action field are generated, clearly presenting the evolutionary process and response characteristics of the action field of each functional unit.

[0047] Step S42: Extract stable evolution response features of functional units from the field evolution response feature data of functional units to generate stable evolution response feature data of functional units; In this embodiment of the invention, the stable evolution response features of the functional unit action field evolution response feature data are extracted. A stable feature screening model is adopted, and the stable evolution judgment criterion is set as the change amplitude of the functional unit action field response parameter does not exceed 5% within 12 consecutive hours. The stable evolution stage and corresponding stable response features of each functional unit in the evolution process are screened out. For functional gene transcription units, a stable evolutionary phase of 12-36 hours was selected. During this phase, transcription efficiency remained at 95%-105% of the normal level, the intensity of the gene transcription action zone remained stable, and the fluctuation range of gene expression level did not exceed 3%. Stable response characteristic parameters such as transcription efficiency, action field strength, and gene expression level were extracted for this phase. For key protein action units, a stable evolutionary phase of 10-40 hours was selected. During this phase, enzyme activity remained at 90%-110% of the normal level, the intensity of the protein catalytic action zone was stable, and the molecular docking binding energy remained between -8.0 kcal / mol and -7.5 kcal / mol. Stable response characteristic parameters such as enzyme activity, action field strength, and molecular docking binding energy were extracted for this phase. For precursor material transformation and molecular docking units, a stable evolutionary phase of 8-42 hours was selected. During this phase, molecular docking efficiency remained at 90%-115% of the normal level, the intensity of the molecular docking and material transformation zone was stable, and the fluctuation range of precursor material transformation rate did not exceed 4%. Stable response characteristic parameters such as docking efficiency, action field strength, and material transformation rate were extracted for this phase. By removing unstable response data during the evolution process, integrating the stable evolution stages and corresponding stable response characteristic parameters of each functional unit, stable evolution response characteristic data of the functional unit is generated, providing a reliable stable characteristic basis for subsequent stable response extension analysis.

[0048] Step S43: Perform extended feature analysis of the stable response of the functional field based on the stable evolution response feature data of the functional unit, and generate extended feature data of the stable response of the functional field; In this embodiment of the invention, based on the stable evolution response characteristic data of functional units, an extended characteristic analysis of the stable response of functional action fields is carried out. Combined with the multi-source relationship data (cooperative, complementary, and conflicting relationships) of functional unit nodes, the stable evolution response characteristic data of functional units are first analyzed for stable clusters of microbial functional units. Using a clustering analysis method, with a clustering threshold of 0.9, the stable evolution response characteristics of three functional units are clustered into two stable clusters: a gene-protein cooperative stable cluster (containing functional gene transcription units and key protein action units) and a molecular docking-material transformation stable cluster (containing precursor material transformation and molecular docking units). The core stable characteristics and intra-cluster unit association rules of each stable cluster are clarified, and microbial functional unit stable cluster data are generated. Based on this stable cluster data, the abnormal response functional units of the microbial functional action field response characteristic data were screened out. The abnormal response judgment criterion was set as a change in response parameters exceeding 10% outside the stable evolution stage. Abnormal response data of functional gene transcription units in 0-12h and 36-48h were screened out, as were abnormal response data of key protein action units in 0-10h and 40-48h, and abnormal response data of precursor material transformation and molecular docking units in 0-8h and 42-48h, thus generating stable response characteristic data of the functional action field. Then, using stable cluster data of microbial functional units and multi-source relationship data of functional unit nodes, cluster-related functional feature extension analysis was performed on the stable response feature data of functional action field. Gene-protein synergistic stable cluster extension analysis showed the linkage characteristics of gene expression and protein synthesis under synergistic effect, with a linkage coefficient set at 0.88, clarifying that for every 10% increase in gene expression, the expression level of key proteins increases by 8.8% simultaneously. Molecular docking-material transformation stable cluster extension analysis showed the linkage characteristics of docking efficiency and material transformation, with a linkage coefficient set at 0.92, clarifying that for every 10% increase in molecular docking efficiency, the material transformation rate increases by 9.2% simultaneously. The extended stable response feature parameters were integrated to generate extended feature data of stable response of functional action field.

[0049] Step S44: Perform functional domain boundary feature analysis on microbial functional units based on the extended feature data of stable response of functional field, and generate microbial functional domain boundary feature data; In this embodiment of the invention, based on the extended characteristic data of the stable response of the functional field, the functional domain boundary characteristics of microbial functional units are analyzed. For each functional unit and stable cluster, the spatial boundary, characteristic boundary, and functional boundary of its functional domain are defined, and the boundary determination criteria are set as the critical values ​​of the stable response characteristic parameters. For the functional gene transcription unit, its functional domain spatial boundary is from coordinates (-60, 15, 5) to (1260, 25, 15), the characteristic boundary is a transcription efficiency of 85%-115% and a gene expression fluctuation of no more than 5%, and the functional boundary is that it is only responsible for the transcription of penicillin synthesis-related genes and does not participate in other metabolic processes. For the key protein action unit, its functional domain spatial boundary is from coordinates (-25, -20, -15) to (35, 20, 15), the characteristic boundary is an enzyme activity of 85%-115% and molecular docking binding. The energy efficiency ranges from -8.5 kcal / mol to -7.0 kcal / mol, with the functional boundary being solely catalyzing the conversion of penicillin precursors and not participating in other catalytic reactions. For the precursor conversion and molecular docking unit, its functional domain spatial boundary is between coordinates (25, 35, 15) and (35, 45, 25), with a characteristic boundary of molecular docking efficiency of 85%-110% and a material conversion rate fluctuation of no more than 4%. Its functional boundary is solely for achieving the conversion of penicillin precursors to the final product and efficient molecular docking, without participating in other material conversions. For the gene-protein co-stabilizing cluster, its functional domain boundary is the overlapping region of the two unit functional domains, between coordinates (-25, 15, 5) and (35, 25, 15). Its functional boundary is to achieve the synergistic effect of gene transcription and protein synthesis, supporting the initial binding of molecular docking. For the molecular docking-material conversion stabilizing cluster, its functional domain boundary is the functional domain of the unit itself, with the functional boundary being to achieve the synergistic effect of efficient molecular docking and material conversion, supporting the final step of penicillin synthesis. The boundary parameters, boundary ranges, and functional definitions of each functional unit and stable cluster are clearly defined. All boundary feature data are integrated to generate boundary feature data of microbial functional domains, clearly defining the functional range of each functional unit and providing boundary basis for subsequent functionally feasible domain analysis.

[0050] Step S45: Perform microbial functional domain realization analysis based on microbial functional domain boundary feature data to generate microbial functional domain realization data.

[0051] In this embodiment of the invention, based on the boundary feature data of microbial functional domains, a functionally realizable domain analysis of microorganisms is carried out. Combined with the set microbial functional requirements (penicillin synthesis yield not less than 150 mg / L, molecular docking binding energy not higher than -8.0 kcal / mol, fermentation cycle not more than 48 hours), a functionally realizable domain determination model is constructed. The core determination indicators of functionally realizable domains are the stable response characteristic parameters of each functional unit, functional domain boundary parameters, and molecular docking related parameters. For the functional gene transcription unit, its achievable functional range is determined to be a transcription efficiency of 85%-115% and a gene expression level that meets the expression requirements of key genes for penicillin synthesis. The corresponding achievable gene transcription function is the efficient synthesis of penicillin synthase genes. For the key protein action unit, its achievable functional range is determined to be an enzyme activity of 85%-115% and a molecular docking binding energy not higher than -8.0 kcal / mol. The corresponding achievable protein catalytic function is the efficient catalysis of penicillin precursor conversion, ensuring efficient molecular docking stability. For the precursor conversion and molecular docking unit, its achievable functional range is determined to be a molecular docking efficiency of 85%-110% and a substance conversion rate that meets the requirements for penicillin synthesis. The corresponding achievable substance conversion function is the efficient conversion of precursor substances into the final penicillin product. By integrating the achievable functional range, functional domain boundaries, and stable response characteristics of the three functional units, the spatial range of the achievable domain of the microorganism is defined as coordinates (-60, -20, -15) to (1260, 45, 25). The core achievable functions are efficient penicillin synthesis and efficient molecular docking between key proteins and precursor substances. The achievable penicillin synthesis yield ranges from 150 mg / L to 180 mg / L, the molecular docking binding energy ranges from -9.0 kcal / mol to -8.0 kcal / mol, and the fermentation cycle ranges from 36 h to 48 h. By integrating all achievable domain parameters, functional ranges, and judgment results, data on the achievable domain of the microorganism is generated, clearly defining the achievable functional range of this actinomycete in penicillin synthesis and efficient molecular docking. This provides a precise range basis for the functional feature matching and optimization analysis in the subsequent step S5, supporting the intelligent matching of microbial functional features.

[0052] Furthermore, step S43 includes the following steps: Step S431: Perform stable cluster analysis of microbial functional units based on the stable evolution response characteristic data of functional units to generate stable cluster data of microbial functional units; In this embodiment of the invention, based on the stable evolution response characteristic data of functional units, a stable cluster analysis of microbial functional units is carried out. The analysis focuses on three minimal functional units (functional gene transcription unit, key protein action unit, and precursor substance transformation and molecular docking unit) related to penicillin synthesis and efficient molecular docking in actinomycetes. A clustering analysis method is adopted, with a clustering threshold of 0.9, a Euclidean distance as the criterion for determining the clustering distance, and 10 iterations of clustering. Clustering is performed by calculating the similarity of the stable evolution response characteristic parameters of each functional unit. The similarity of stable features between functional gene transcription units and key protein action units was calculated. Based on the overlap duration of their stable evolutionary stages (12-36h and 10-40h overlap for 24h) and the variation of response parameters (gene expression level and enzyme activity are positively correlated), the similarity was 0.92, which is higher than the clustering threshold of 0.9, and the two were grouped into one category. The similarity of stable features between precursor material transformation and molecular docking units and the former two was calculated. Although the overlap duration of their stable evolutionary stages (8-42h) with the former two was long, the correlation between their core response parameters (molecular docking efficiency, material transformation rate) and the former two (transcription efficiency, enzyme activity) was weak, and the similarity was 0.78, which is lower than the clustering threshold of 0.9, and they were grouped into a separate category. Two stable clusters of microbial functional units were ultimately formed: a gene-protein co-operational stable cluster (containing functional gene transcription units and key protein action units) and a molecular docking-material transformation stable cluster (containing precursor material transformation and molecular docking units). The core stability characteristics of each stable cluster were defined: the core characteristics of the gene-protein co-operational stable cluster are transcription efficiency of 95%-105%, enzyme activity of 90%-110%, and molecular docking binding energy of -8.0 kcal / mol to -7.5 kcal / mol. The intra-cluster unit correlation is that gene expression level is positively correlated with key protein expression level. The core characteristics of the molecular docking-material transformation stable cluster are molecular docking efficiency of 90%-115% and material transformation rate fluctuation of no more than 4%. The core intra-cluster unit correlation is that docking efficiency is positively correlated with material transformation rate. By integrating the core characteristics, intra-cluster correlation patterns, and clustering parameters of the two stable clusters, microbial functional unit stable cluster data were generated, providing a cluster classification basis for subsequent abnormal response screening and functional feature expansion.

[0053] Step S432: Based on the stable cluster data of microbial functional units, the abnormal response functional units of the microbial functional field response characteristic data are screened out to generate stable response characteristic data of the functional field. In this embodiment of the invention, based on stable cluster data of microbial functional units, abnormal response functional units in the microbial functional field response characteristic data are screened out. The core logic of abnormal response screening is to remove response data of each functional unit within a stable cluster that is outside the stable evolution stage and whose response parameters fluctuate beyond a reasonable range, ensuring that the screened data are all stable response data corresponding to stable clusters. The abnormal response judgment criterion is set as a response parameter change exceeding 10% outside the stable evolution stage. Combined with the stable evolution stage of the functional units corresponding to two stable clusters, abnormal response data is screened out one by one. For functional gene transcription units in the gene-protein co-stabilization cluster, the stable evolution phase is 12-36h. Response data with transcription efficiency changes exceeding 10% outside this phase (0-12h, 36-48h) are screened out, with a focus on screening out response data with transcription efficiency below 85% in 0-12h and above 115% in 36-48h. For key protein action units, the stable evolution phase is 10-40h. Response data with enzyme activity changes exceeding 10% and molecular docking binding energy fluctuations exceeding 0.5kcal / mol outside this phase (0-10h, 40-48h) are screened out. For precursor material conversion and molecular docking units in the molecular docking-material conversion stable cluster, the stable evolution phase is 8-42h. Response data with molecular docking efficiency changes exceeding 10% and material conversion rate fluctuations exceeding 4% outside this phase (0-8h, 42-48h) are screened out. During the screening process, the abnormal data types, abnormal parameter ranges, and corresponding time periods of the screened abnormal data are recorded simultaneously. Stable response data of each functional unit within the stable evolution stage with response parameter changes not exceeding 10% are retained. All retained stable response data are integrated to generate stable response characteristic data of the functional action field, ensuring that the data accurately corresponds to the stable evolution characteristics of the stable cluster, and providing a reliable data foundation for subsequent functional characteristic expansion analysis.

[0054] Step S433: Perform cluster-related functional feature extension analysis on the stable cluster data of microbial functional units and multi-source relationship data of functional unit nodes to generate extended feature data of stable response of functional field.

[0055] In this embodiment of the invention, based on both stable cluster data of microbial functional units and multi-source relationship data (cooperative, complementary, and conflicting relationships) of functional unit nodes, cluster-related functional feature extension analysis is performed on the stable response feature data of the functional field generated in step S432. The extension logic is based on the association rules of units within the stable cluster and the multi-source relationships of functional unit nodes to explore the linkage relationship of stable response features, extend the coverage of stable response features, and improve the stable response feature system of the functional field. For gene-protein cooperative stable clusters, combined with the cooperative and complementary relationships in the multi-source relationships of functional unit nodes, the linkage characteristics of gene expression and protein synthesis under their cooperative effect are extended for analysis. The linkage coefficient is set at 0.88, clarifying that for every 10% increase in gene expression, the expression level of key proteins increases by 8.8% simultaneously. At the same time, the linkage rules between gene transcription efficiency and enzyme activity are extended for analysis, setting that for every 10% increase in transcription efficiency, enzyme activity increases by 8.5% simultaneously. The influence of gene-protein cooperative effect on molecular docking binding energy is further clarified. When both transcription efficiency and enzyme activity are within a stable range, the molecular docking binding energy is maintained between -8.0 kcal / mol and -7.5 kcal / mol. For the molecular docking-material transformation stable cluster, combined with the synergistic relationship in the multi-source relationship of functional unit nodes, the linkage characteristics between docking efficiency and material transformation are expanded and analyzed. A linkage coefficient of 0.92 is set, clarifying that for every 10% increase in molecular docking efficiency, the material transformation rate increases by 9.2%. Simultaneously, the linkage law between molecular docking binding energy and material transformation rate is further analyzed, setting a 10% increase in material transformation rate for every 0.5 kcal / mol decrease in molecular docking binding energy, clarifying the supporting role of efficient molecular docking in the transformation of penicillin precursors. The expanded linkage characteristics, linkage coefficients, and correlation laws of the two stable clusters are integrated and supplemented into the functional field stability response characteristic data, generating extended characteristic data of functional field stability response. This retains the original stability response characteristics while improving the linkage characteristics of units within the cluster, providing a more comprehensive stable response basis for subsequent functional domain boundary analysis and functionally realizable domain analysis, supporting the precise and intelligent matching of microbial functional characteristics.

[0056] Furthermore, step S5 includes the following steps: Step S51: Perform logical constraint analysis on the target functional requirements based on the microbial functional requirement data to generate logical constraint data for the target functional requirements; In this embodiment of the invention, based on microbial functional requirement data, a logical constraint analysis of the target functional requirements is conducted. The core logic involves decomposing the core indicators of the target functional requirements, clarifying the constraints of each indicator and the logical relationships between them, ensuring that the constraint analysis aligns with the intelligent matching requirements of penicillin synthesis and efficient molecular docking in actinomycetes, and providing a clear logical basis for subsequent matching and optimization combinations. The target functional requirement is defined as screening actinomycetes that can efficiently synthesize penicillin and achieve efficient molecular docking. Specific indicators are limited to penicillin synthesis yield not less than 150 mg / L, molecular docking binding energy not higher than -8.0 kcal / mol, and fermentation cycle not exceeding 48 hours. The logical constraint analysis revolves around these three core indicators, clarifying the constraint boundaries of each indicator and the synergistic constraint relationships between them. The constraint boundary for penicillin synthesis yield is set at 150 mg / L-180 mg / L. This constraint is based on the minimum yield requirement for industrial penicillin production; yields below 150 mg / L cannot meet the needs of large-scale production. The constraint boundary for molecular docking binding energy is -9.0 kcal / mol to -8.0 kcal / mol. This constraint is based on the stability requirement for efficient docking between the key protein and the penicillin precursor molecule. When the binding energy is higher than -8.0 kcal / mol, the molecular docking stability is insufficient, and the efficiency of penicillin synthesis cannot be guaranteed. The constraint boundary for fermentation cycle is 36 h-48 h. This constraint is based on the efficiency requirements of industrial production. Fermentation cycle exceeding 48 h will increase production costs, while fermentation cycle below 36 h will result in insufficient penicillin synthesis. At the same time, the logical relationship between the three indicators is clarified: only when the molecular docking binding energy is within the constraint range can the penicillin synthesis yield reach more than 150 mg / L and the fermentation cycle be controlled within 48 hours; when the fermentation cycle is controlled between 36 hours and 48 hours, it can ensure sufficient penicillin synthesis and avoid the decline in molecular docking efficiency. The constraint boundaries, logical relationships and constraint conditions of each indicator are integrated to generate logical constraint data of target functional requirements and clarify the core constraint standards of intelligent matching.

[0057] Step S52: Map the target functional requirement logical constraint data to the microbial functional realizable domain data, perform optimization combination analysis of microbial functional feature matching, and generate microbial functional feature matching optimization combination data.

[0058] In this embodiment of the invention, the logical constraints of the target functional requirements are precisely mapped to the functionally realizable domain data of microorganisms. An optimized combination analysis of microbial functional features is then conducted. The core logic is based on logical constraint standards, selecting the combination of microbial functional features that best matches the target requirements from the functionally realizable domain. This achieves precise and intelligent matching between the functional features of actinomycetes and the requirements for penicillin synthesis and efficient molecular docking. The functionally realizable domain data of microorganisms clearly shows that the achievable penicillin synthesis yield of actinomycetes ranges from 150 mg / L to 180 mg / L, the molecular docking binding energy ranges from -9.0 kcal / mol to -8.0 kcal / mol, and the fermentation cycle ranges from 36 h to 48 h, perfectly matching the logical constraints of the target functional requirements. Based on this, an optimized combination analysis is conducted. The core objectives of the optimized combination are set as maximizing penicillin synthesis yield, improving molecular docking stability, and shortening the fermentation cycle. The optimization weights are set as follows: penicillin synthesis yield weight 0.4, molecular docking binding energy weight 0.35, and fermentation cycle weight 0.25, with a total weight of 1. For the functional gene transcription unit, characteristic parameters with transcription efficiency of 100%-110% were screened. The gene expression level corresponding to this parameter can support the efficient synthesis of penicillin synthase and ensure penicillin yield. For the key protein action unit, characteristic parameters with enzyme activity of 100%-110% and a stable active site were screened. This parameter can improve the molecular docking stability between the key protein and penicillin precursor, maintaining the molecular docking binding energy at around -8.5 kcal / mol. For the precursor conversion and molecular docking unit, characteristic parameters with molecular docking efficiency of 100%-105% and a stable conversion rate were screened. This parameter can accelerate the conversion of penicillin precursor and control the fermentation cycle between 40h and 42h. By integrating the optimized characteristic parameters of the three functional units, an optimal combination of functional characteristics was formed: functional gene transcription efficiency of 105%, key protease activity of 105%, and molecular docking efficiency of 102%, corresponding to a penicillin synthesis yield of 170 mg / L, molecular docking binding energy of -8.5 kcal / mol, and fermentation cycle of 41 h. This combination fully meets the logical constraints of the target functional requirements. By integrating all characteristic parameters, matching results, and optimization basis of this combination, microbial functional characteristic matching optimization combination data were generated, providing precise technical support for microbial screening and application.

[0059] This specification provides a microbial functional characteristic intelligent matching system for executing the microbial functional characteristic intelligent matching method as described above. The microbial functional characteristic intelligent matching system includes: The microbial functional unit description and analysis module is used to acquire target microbial samples and collect basic microbial sample data and microbial functional requirement data; it performs structural description feature analysis on the basic microbial sample data to generate microbial functional unit structural description feature data. The microbial functional field analysis module is used to perform microbial functional field analysis and processing based on the structural description feature data of microbial functional units, and generate microbial functional field data. The action field response feature analysis module is used to perform microbial functional action field response feature analysis on microbial functional action field data and generate microbial functional action field response feature data. The Functional Realization Domain Analysis module is used to perform functional realization domain analysis of microorganisms based on microbial functional action field response characteristic data, and generate microbial functional realization domain data; The microbial functional feature matching module is used to perform optimal combination analysis of microbial functional feature matching on microbial functional requirement data and microbial functional realization domain data, and generate microbial functional feature matching optimal combination data.

[0060] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0061] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for intelligent matching of microbial functional characteristics, characterized in that, Includes the following steps: Step S1: Obtain the target microbial sample and collect the basic data of the target microbial sample and the microbial functional requirement data; perform structural description feature analysis of the microbial functional units on the basic data of the microbial sample to generate structural description feature data of the microbial functional units; Step S2: Perform microbial functional field analysis based on the structural description feature data of microbial functional units to generate microbial functional field data; Step S3: Perform microbial functional field response characteristic analysis on the microbial functional field data to generate microbial functional field response characteristic data; Step S4: Perform microbial functional realization domain analysis based on microbial functional action field response characteristic data to generate microbial functional realization domain data; Step S5: Perform optimization combination analysis on the microbial functional requirement data and the microbial functional realization domain data to generate microbial functional feature matching optimization combination data.

2. The intelligent matching method for microbial functional characteristics according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain the target microbial sample and collect the basic microbial sample data and microbial functional requirement data of the target microbial sample; Step S12: Perform multimodal microbial feature analysis based on the basic data of microbial samples to generate multimodal microbial feature data; Step S13: Perform microbial functional description analysis based on microbial multimodal characteristic data to generate microbial functional description data; Step S14: Perform functional description behavior parsing on the microbial functional description data to generate microbial functional description behavior data, and use the microbial functional description behavior data to establish the minimum functional unit of microbial functional description in the microbial functional description data to generate microbial functional unit description data. Step S15: Perform structural description feature analysis of microbial functional units based on the microbial functional unit description data to generate microbial functional unit structural description feature data.

3. The intelligent matching method for microbial functional characteristics according to claim 2, characterized in that, The microbial multimodal characteristic data mentioned in step S12 includes microbial genome sequence characteristic data, microbial protein expression characteristic data, and microbial metabolite characteristic data.

4. The intelligent matching method for microbial functional characteristics according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Perform spatial coordinate mapping processing on the functional unit structure description feature data of microbial functional units to generate spatial mapping data of functional unit structure description; Step S22: Perform multi-source relationship analysis on the functional unit nodes based on the functional unit structure description spatial mapping data to generate multi-source relationship data of functional unit nodes; Step S23: Perform multi-source relation weighting processing on the multi-source relation data of functional unit nodes to generate multi-source relation weighted data of functional unit nodes; Step S24: Perform spatial neighborhood influence intensity analysis on the functional unit relationship based on the multi-source relationship weighted data of functional unit nodes, and generate spatial influence intensity data of functional unit action; Step S25: Simulate the diffusion distribution of the influence of functional units based on the spatial influence intensity data of functional units, and generate diffusion distribution data of the influence of functional units. Step S26: Analyze the microbial functional field by analyzing the diffusion distribution data of the influence of functional units, and generate microbial functional field data.

5. The intelligent matching method for microbial functional characteristics according to claim 4, characterized in that, The multi-source relationships of functional unit nodes mentioned in step S22 include functional unit node collaborative relationship data, functional unit node complementary relationship data, and functional unit node conflict relationship data.

6. The intelligent matching method for microbial functional characteristics according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Design microbial functional perturbation parameters using microbial functional unit structure description feature data and microbial functional field data; Step S32: Analyze the perturbation state of functional units in the microbial functional field based on the microbial functional perturbation parameters, and generate functional unit perturbation state data of the field. Step S33: Analyze the propagation impact of the disturbance state of the functional unit in the action field based on the disturbance state data of the functional unit in the action field, and generate disturbance propagation impact data of the functional unit in the action field. Step S34: Based on the disturbance propagation impact data of the functional unit of the action field, perform disturbance-specific propagation trajectory characteristic analysis of the functional unit of the action field to generate disturbance action field functional unit propagation trajectory characteristic data; Step S35: Analyze the response characteristics of the microbial functional field by using the propagation trajectory characteristic data of the perturbation field functional unit, and generate microbial functional field response characteristic data.

7. The intelligent matching method for microbial functional characteristics according to claim 4, characterized in that, Step S4 includes the following steps: Step S41: Analyze the evolution response characteristics of functional units based on the microbial functional field response characteristic data, and generate functional unit functional field evolution response characteristic data; Step S42: Extract stable evolution response features of functional units from the field evolution response feature data of functional units to generate stable evolution response feature data of functional units; Step S43: Perform extended feature analysis of the stable response of the functional field based on the stable evolution response feature data of the functional unit, and generate extended feature data of the stable response of the functional field; Step S44: Perform functional domain boundary feature analysis on microbial functional units based on the extended feature data of stable response of functional field, and generate microbial functional domain boundary feature data; Step S45: Perform microbial functional domain realization analysis based on microbial functional domain boundary feature data to generate microbial functional domain realization data.

8. The intelligent matching method for microbial functional characteristics according to claim 7, characterized in that, Step S43 includes the following steps: Step S431: Perform stable cluster analysis of microbial functional units based on the stable evolution response characteristic data of functional units to generate stable cluster data of microbial functional units; Step S432: Based on the stable cluster data of microbial functional units, the abnormal response functional units of the microbial functional field response characteristic data are screened out to generate stable response characteristic data of the functional field. Step S433: Perform cluster-related functional feature extension analysis on the stable cluster data of microbial functional units and multi-source relationship data of functional unit nodes to generate extended feature data of stable response of functional field.

9. The intelligent matching method for microbial functional characteristics according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Perform logical constraint analysis on the target functional requirements based on the microbial functional requirement data to generate logical constraint data for the target functional requirements; Step S52: Map the target functional requirement logical constraint data to the microbial functional realizable domain data, perform optimization combination analysis of microbial functional feature matching, and generate microbial functional feature matching optimization combination data.

10. A microbial functional characteristic intelligent matching system, characterized in that, For executing the intelligent matching method of microbial functional characteristics as described in claim 1, the intelligent matching system of microbial functional characteristics includes: The microbial functional unit description and analysis module is used to acquire target microbial samples and collect basic microbial sample data and microbial functional requirement data; it performs structural description feature analysis on the basic microbial sample data to generate microbial functional unit structural description feature data. The microbial functional field analysis module is used to perform microbial functional field analysis and processing based on the structural description feature data of microbial functional units, and generate microbial functional field data. The action field response feature analysis module is used to perform microbial functional action field response feature analysis on microbial functional action field data and generate microbial functional action field response feature data. The Functional Realization Domain Analysis module is used to perform functional realization domain analysis of microorganisms based on microbial functional action field response characteristic data, and generate microbial functional realization domain data; The microbial functional feature matching module is used to perform optimal combination analysis of microbial functional feature matching on microbial functional requirement data and microbial functional realization domain data, and generate microbial functional feature matching optimal combination data.

Images